Imagine trying to solve a puzzle with missing pieces. That's the challenge facing energy providers when dealing with incomplete load profiles – the records of energy consumption over time. These gaps in the data, often caused by equipment malfunctions or planned interventions like demand response programs, can significantly hinder important tasks such as modeling load behaviors and designing efficient energy distribution systems. Traditionally, filling these gaps has relied on complex, data-hungry methods. But what if we could leverage the power of AI to complete the puzzle with far fewer pieces? This is the premise of a groundbreaking new study exploring the use of fine-tuned Large Language Models (LLMs) like GPT-3.5 for missing data restoration in power systems. LLMs, known for their ability to understand and generate human-like text, are now showing promise in a whole new arena: making sense of energy consumption patterns. The research introduces a two-stage fine-tuning strategy. First, a general LLM is trained on data from a small group of users similar to the target user. Then, this model is further refined using a tiny amount of data from the target user themselves. Surprisingly, the study found that using massive datasets isn't always necessary. The fine-tuned LLM can accurately restore missing data segments using a fraction of the data required by traditional methods. This is achieved by cleverly encoding the load and temperature data into a format that the LLM can understand and then “prompting” the model with instructions. This few-shot learning approach significantly reduces the data needs and training time, making it a practical solution for real-world applications. The findings have significant implications for the energy sector. By accurately filling data gaps, energy providers can better understand consumer behavior, optimize energy distribution, and enhance grid resilience. While the results of this study are promising, the researchers acknowledge that further improvements are possible. Future research aims to expand the use of LLMs to other critical tasks in power systems, such as load forecasting and customer segmentation. The ability of LLMs to learn complex patterns from limited data opens up exciting new possibilities for addressing the challenges of data scarcity and efficient resource management. This research offers a glimpse into the potential of AI not just to analyze energy data, but to complete the picture, enabling a more intelligent and resilient energy future.